求解多维背包问题的蚁群-拉格朗日松弛混合优化算法

针对多维背包问题(MKP) NP-hard、约束强的特点, 提出一种高效的蚁群-拉格朗日松弛(LR) 混合优化算法. 该算法以蚁群优化(ACO) 为基本框架, 并基于LR 对偶信息定义了一种MKP效用指标. ACO使得整体算法具有全局搜索能力, 所设计的效用指标将MKP的优化目标与约束条件有机地融合在一起. 该指标一方面可以用来定 义MKP核问题, 降低问题规模; 另一方面, 可以用作ACO的启发因子, 引导算法在有希望的解区域中强化搜索. 在大量标准算例上的测试结果表明, 所提出算法的鲁棒性较好; 与其他已有算法相比, 在求解质量和求解效率方面均具有很强的竞争力.

     

基于有导向变异算子求解多维背包问题

多维背包问题(MKP)是经典的NP难的组合优化问题。引入有导向变异算子的进化算法GM-EA(Guided Mutation EA)来求解该问题,通过结合粒子群优化的方法改进郭涛算法,更好地利用种群中的全局信息,取得较好的效果。实验结果表明GM-EA是求解MKP有效的算法。     

多维背包问题的一个蚁群优化算法

蚁群优化(ACO)是一种通用的启发式方法,已被用来求解很多离散优化问题.近年来,已提出几个ACO算法求解多维背包问题(MKP).这些算法虽然能获得较好的解但也耗用太多的CPU时间.为了降低用ACO求解MKP的复杂性,文章基于一种已提出但未实现过的MKP的信息素表示定义了新的选择概率的规则和相应的基于背包项的一种序的启发式信息,从而提出了一种计算复杂性较低、求解性能较好的改进型蚁群算法.实验结果表明,无论串行执行还是虚拟并行执行,在计算相同任务时,新算法耗用时间少且解的价值更高.不仅如此,在实验中,文中的新算法获得了ORLIB中测试算例5.250-22的两个"新"解.     

改进的差分演化算法求解多维背包问题

多维背包（MKP）是组合优化中一个典型的NP难问题,广泛应用于工程和管理中。提出了一种改进的二进制差分演化算法（Modified Binary Differential Evolution algorithm,MBDE）求解MKP问题,算法关键步骤可分为两部分：二进制群体生成;得到候选可行解。提出了一种有效的衡量商品价值密度的方法用于对二进制个体修正和优化;设计了反向测试搜索和精英局部搜索策略来提高算法探索和开发能力,从而进一步提高了MBDE的求解精度和收敛速度。为验证MBDE算法的有效性,进行了三组实验,并和近期提出的解决MKP问题的其他启发式算法进行了比较,实验结果显示,MBDE算法求解精度更高。从算法运行时间看,求解速度快,非常适合求解大规模的MKP问题。     

基于模运算的新颖离散差分演化算法求解多背包问题

多背包问题（MKP）是一个求解难度极大的背包问题。为了基于差分演化（DE）求解MKP,首先建立了MKP的整数规划模型,在利用模运算构造简单且有效的新型传递函数基础上,提出了一个新颖离散差分演化算法MODDE;基于贪心策略提出了消除MKP不可行解的一个有效算法GROA,由此利用MODDE给出了求解MKP的一种新方法。最后,利用MODDE求解30个国际通用的MKP实例,通过与四个代表性演化算法的比较表明,MODDE不仅计算结果优,而且算法的稳定性强,是求解MKP的一个高效算法。     

一种求解多维背包问题的混合分布估计算法

针对多维背包问题（MKP）,提出一种基于分布估计算法的混合求解算法,该算法基于优势种群构建概率模型,并基于概率模型采样产生新个体;同时,提出一种基于MKP问题信息的修复机制,有效修复采样后种群中的不可行解．另外,设计了一种自适应的局部搜索操作,以增强算法的局部搜索能力,基于标准测试集的仿真结果和算法比较验证了所提出的混合算法的有效性和鲁棒性．     

贪心二进制狮群优化算法求解多维背包问题

针对经典的多约束组合优化问题——多维背包问题(MKP),提出了一种贪心二进制狮群优化(GBLSO)算法。首先,采用二进制代码转换公式将狮群个体位置离散化,得到二进制的狮群算法;其次,引入反置移动算子对狮王位置进行更新,同时对母狮和幼狮位置重新定义;然后,充分利用贪心算法进行解的可行化处理,增强搜索能力并进一步提高收敛速度;最后,对10个MKP典型算例进行仿真实验,并把GBLSO算法与离散二进制粒子群(DPSO)算法和二进制蝙蝠算法(BBA)进行对比。实验结果表明,GBLSO算法是一种有效的求解MKP的新方法,在求解MKP时具有相对良好的收敛效率、较高的寻优精度和很好的鲁棒性。     

改进二进制和声搜索算法求解多维背包问题

和声搜索（HS）是一种已广泛应用于连续优化问题的元启发式方法。针对典型的组合优化问题——多维背包问题（MKP）,提出一种改进二进制和声搜索（IBHS）算法。算法通过伯努利随机过程生成二进制群体,在候选和声生成算子中,引入动态自适应参数,通过算法参数的自适应调整来协调算法的全局搜索和局部搜索,并提出一种新的更有效的衡量商品多维加权价值密度的方法用于二进制个体修正和优化;引入精英局部搜索机制进行协同寻优,提高IBHS的收敛速度。通过求解10组不同规模的典型多维背包算例和与贪心二进制狮群优化（GBLSO）算法、改进的差分演化（MBDE）算法以及二进制修正和声（BMHS）算法的对比分析,实验结果表明,所提算法在求解MKP时有具有良好的收敛效率、较高的寻优精度和很好的鲁棒性。     

求解多维背包问题的二级协作果蝇优化算法

针对多维背包问题(MKP)约束性强和复杂度高的特点,提出一种新型二级协作果蝇优化算法(TCFOA).提出一级果蝇和二级果蝇的产生机制,将二级果蝇划分为开发用果蝇和探索用果蝇两类以协调开发与探索之间的平衡;设计果蝇交流策略以及基于全局性价比的解的修复补偿机制,并利用二级结构扩大搜索范围、改善一级果蝇的质量,以提高求解质量.基于MKP两个标准测试集的测试结果和算法性能对比,表明TCFOA在求解MKP方面具有较强的优势.     

基于引导素更新和扩散机制的人工蜂群算法

人工蜂群算法是一种新型的搜索算法,其机理是通过模拟蜂群采蜜过程中体现出的智能行为来实现对问题的求解.在现有的蜂群算法中,蜂群间的信息交流仅使用单一的行为通信(跳舞),蜂群间的协作存在明显不足,影响了蜂群算法的求解性能.根据真实蜜蜂多模式传递信息的客观事实,通过引入基于引导素的化学通信方式,提出一种新的更忠实反映蜂群信息传递的蜂群算法,并应用于多维背包问题(MKP)的求解.新算法将行为通信和化学通信相融合,利用引导素的更新和扩散机制使蜂群能够更好地进行协作.MKP的仿真实验结果表明新算法优于传统的ABC算法.与其他一些元启发式搜索算法的比较同样显示了新算法的有效性.     

Improving problem reduction for 0–1 Multidimensional Knapsack Problems with valid inequalities

This paper investigates the problem reduction heuristic for the Multidimensional Knapsack Problem (MKP). The MKP formulation is first strengthened by the Global Lifted Cover Inequalities (GLCI) using the cutting plane approach. The dynamic core problem heuristic is then applied to find good solutions. The GLCI is described in the general lifting framework and several variants are introduced. A Two-level Core problem Heuristic is also proposed to tackle large instances. Computational experiments were carried out on classic benchmark problems to demonstrate the effectiveness of this new method.     

Water wave optimization for combinatorial optimization: Design strategies and applications

Although the community of nature-inspired computing has witnessed a wide variety of metaheuristics, it often requires considerable effort to adapt them to different combinatorial optimization problems (COPs), and few studies have been devoted to reducing this burden. This paper proposes a systematic approach that consists of a set of basic steps and strategies for adapting water wave optimization (WWO), a simple and generic metaheuristic, to concrete heuristic algorithms for different COPs. Taking advantages of the generic algorithmic framework, designers can only focus on adapting the prorogation operator and the wavelength calculation method according to the combinatorial properties of the given problem, and thus easily derive efficient problem-solving algorithms. We illustrate and test our approach on the flow-shop scheduling problem (FSP), the single-objective multidimensional knapsack problem (MKP), and the multi-objective MKP, and then present an application to a machine utilization optimization problem for a large manufacturing enterprise. The results demonstrate that our approach can derive concrete algorithms that are competitive to the state-of-the-arts. Our approach also provides insights into the adaptation of other metaheuristics and the development of new metaheuristics for COPs.     

A New Heuristic for Solving the Multichoice Multidimensional Knapsack Problem

A new heuristic for solving the multichoice multidimensional knapsack problem (MMKP) is presented in this paper. The MMKP is first reduced to a multidimensional knapsack problem (MKP). A linear programming relaxation of the resulting MKP is solved, and a series of new values for the variables is computed. These values, pseudo-utility values, and resource value coefficients computed as well, are used in order to obtain a feasible solution for the original MMKP. Finally, the quality of the feasible solution is improved using the pseudo-utility values and the coefficient values of the objective function. Numerical results show that the performance of this approach is superior to that of previous techniques.     

A novel strategy for automatic test data generation using soft computing technique

Software testing is one of the most crucial and analytical aspect to assure that developed software meets prescribed quality standards. Software development process invests at least 50% of the total cost in software testing process. Optimum and efficacious test data design of software is an important and challenging activity due to the nonlinear structure of software. Moreover, test case type and scope determines the quality of test data. To address this issue, software testing tools should employ intelligence based soft computing techniques like particle swarm optimization (PSO) and genetic algorithm (GA) to generate smart and efficient test data automatically. This paper presents a hybrid PSO and GA based heuristic for automatic generation of test suites. In this paper, we described the design and implementation of the proposed strategy and evaluated our model by performing experiments with ten container classes from the Java standard library. We analyzed our algorithm statistically with test adequacy criterion as branch coverage. The performance adequacy criterion is taken as percentage coverage per unit time and percentage of faults detected by the generated test data. We have compared our work with the heuristic based upon GA, PSO, existing hybrid strategies based on GA and PSO and memetic algorithm. The results showed that the test case generation is efficient in our work.     

Glass container production scheduling through hybrid multi-population based evolutionary algorithm

Driven by a real-world application in the capital-intensive glass container industry, this paper provides the design of a new hybrid evolutionary algorithm to tackle the short-term production planning and scheduling problem. The challenge consists of sizing and scheduling the lots in the most cost-effective manner on a set of parallel molding machines that are fed by a furnace that melts the glass. The solution procedure combines a multi-population hierarchically structured genetic algorithm (GA) with a simulated annealing (SA), and a tailor-made heuristic named cavity heuristic (CH). The SA is applied to intensify the search for solutions in the neighborhood of the best individuals found by the GA, while the CH determines quickly values for a relevant decision variable of the problem: the processing speed of each machine. The results indicate the superior performance of the proposed approach against a state-of-the-art commercial solver, and compared to a non-hybridized multi-population GA.     

用Coop＆compEA解决三维装箱问题

三维装箱问题是一类典型的NP-complete问题。该文通过一种新协同进化算法Coop&compEA与一组启发式规则相结合,给出一类典型装箱问题的求解策略。在该求解策略中,利用Coop&compEA算法将种群层的竞争通过反馈引入个体层的合作进化过程,既优化了合作进化的求解质量,又融入了竞争带来的快速收敛效果;此外补充了更加恰当的装箱启发规则。实验结果显示,这样的求解策略无论是进化速度还是求解效果都优于之前的传统GA方法和CCGA方法。     

A new hybrid combinatorial genetic algorithm for multidimensional knapsack problems

Multidimensional knapsack problem (MKP) is known to be a NP-hard problem, more specifically a NP-complete problem, which cannot be resolved in polynomial time up to now. MKP can be applicable in many management, industry and engineering fields, such as cargo loading, capital budgeting and resource allocation, etc. In this article, using a combinational permutation constructed by the convex combinatorial value \(M_j=(1-\lambda ) u_j+ \lambda x^\mathrm{LP}_j\) of both the pseudo-utility ratios of MKP and the optimal solution \(x^\mathrm{LP}\) of relaxed LP, we present a new hybrid combinatorial genetic algorithm (HCGA) to address multidimensional knapsack problems. Comparing to Chu’s GA (J Heuristics 4:63–86, 1998), empirical results show that our new heuristic algorithm HCGA obtains better solutions over 270 standard test problem instances.     

A new hybrid GA solution to combinatorial optimization problems— an application to the multiprocessor scheduling problem

The multiprocessor scheduling problem is one of the classic examples of NP-hard combinatorial optimization problems. Several polynomial time optimization algorithms have been proposed for approximating the multiprocessor scheduling problem. In this paper, we suggest a geneticizedknowledge genetic algorithm (gkGA) as an efficient heuristic approach for solving the multiprocessor scheduling and other combinatorial optimization problems. The basic idea behind the gkGA approach is that knowledge of the heuristics to be used in the GA is also geneticized alongiside the genetic chromosomes. We start by providing four conversion schemes based on heuristics for converting chromosomes into priority lists. Through experimental evaluation, we observe that the performance of our GA based on each of these schemes is instance-dependent. However, if we simultaneously incorporate these schemes into our GA through the gkGA approach, simulation results show that the approach is not problem-dependent, and that the approach outperforms that of the previous GA. We also show the effectiveness of the gkGA approach compared with other conventional schemes through experimental evaluation. This work was presented, in part, at the Second International Symposium on Artifiical Life and Robotics, Oita, Japan, February 18–20, 1997     

Learning with case-injected genetic algorithms

This paper presents a new approach to acquiring and using problem specific knowledge during a genetic algorithm (GA) search. A GA augmented with a case-based memory of past problem solving attempts learns to obtain better performance over time on sets of similar problems. Rather than starting anew on each problem, we periodically inject a GA's population with appropriate intermediate solutions to similar previously solved problems. Perhaps, counterintuitively, simply injecting solutions to previously solved problems does not produce very good results. We provide a framework for evaluating this GA-based machine-learning system and show experimental results on a set of design and optimization problems. These results demonstrate the performance gains from our approach and indicate that our system learns to take less time to provide quality solutions to a new problem as it gains experience from solving other similar problems in design and optimization.